On cherche à étudier l’effet de trois facteurs sur le transcriptome des racines d’Arabidopsis thaliana et de la micro Tomate.

CO2

Clustering

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4.90653064844082e-08"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 9.85664883046411e-11"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.33795765577815e-08"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.30698684997333e-09"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 2.8421709430404e-14"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.6843418860808e-14"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.79953033807578e-06"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -740101.6
*************************************************
Number of clusters = 12
ICL = -740101.6
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
         4         22         25          5          2          3          8 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
        12          7         17         12         14 

Number of observations with MAP > 0.90 (% of total):
130 (99.24%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 4         22        25        5         2         3         8        
 (100%)    (100%)    (100%)    (100%)    (100%)    (100%)    (100%)   
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 12        7         17         11         14        
 (100%)    (100%)    (100%)     (91.67%)   (100%)    

Model-Based Clustering Using MPLN (Parallelized) Description Performs clustering using mixtures of multivariate Poisson-log normal (MPLN) distribution and model selection using AIC, AIC3, BIC and ICL. Since each component/cluster size (G) is independent from another, all Gs in the range to be tested have been parallelized to run on a seperate core using the parallel R package.

Visualisation en ACP

Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
    Ax1     Ax2     Ax3     Ax4     Ax5 
17.2861  4.7060  0.6320  0.5028  0.2615 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
 72.025  19.608   2.633   2.095   1.089 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  72.03   91.63   94.27   96.36   97.45 

(Only 5 dimensions (out of 24) are shown)

NULL

Nitrate

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4.59017712728382e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0857992577874072"
[1] "Log-like diff: 0.0273270790755795"
[1] "Log-like diff: 0.00026883443707959"
[1] "Log-like diff: 3.19062152343008e-06"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.21945280540103e-07"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 2.64516266668124e-08"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 8.91520812729141e-09"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 3.85815361880759e-08"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.731106217676853"
[1] "Log-like diff: 0.876331706008131"
[1] "Log-like diff: 1.54299612519634"
[1] "Log-like diff: 3.0168944802933"
[1] "Log-like diff: 5.52771074616917"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.000391092955158712"
[1] "Log-like diff: 9.18728272125691e-06"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 5.83051829039505e-08"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 2.47112143441086e-05"
[1] "Log-like diff: 1.4252105380308e-06"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.00558365885393997"
[1] "Log-like diff: 0.00168267525018351"
[1] "Log-like diff: 0.000489232319583977"
[1] "Log-like diff: 0.000153918916637963"
[1] "Log-like diff: 3.92342648254385e-05"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3013888
*************************************************
Number of clusters = 12
ICL = -3013888
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
       141         21         32         24        118        175        100 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
        63         76          7         33         47 

Number of observations with MAP > 0.90 (% of total):
833 (99.52%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 139       21        32        24        118       174       100      
 (98.58%)  (100%)    (100%)    (100%)    (100%)    (99.43%)  (100%)   
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 63        76        7          33         46        
 (100%)    (100%)    (100%)     (100%)     (97.87%)  
Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
    Ax1     Ax2     Ax3     Ax4     Ax5 
19.1712  3.3535  0.5249  0.3943  0.1205 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
 79.880  13.973   2.187   1.643   0.502 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  79.88   93.85   96.04   97.68   98.18 

(Only 5 dimensions (out of 24) are shown)

NULL

Iron

****************************************
coseq analysis: Poisson approach & none transformation
K = 2 to 12 
Use set.seed() prior to running coseq for reproducible results.
****************************************
Running g = 2 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.06819442180495e-10"
Running g = 3 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0758628365212921"
[1] "Log-like diff: 0.0198623147528725"
[1] "Log-like diff: 0.00525704057190701"
[1] "Log-like diff: 0.00139919104513453"
[1] "Log-like diff: 0.000341532015648127"
Running g = 4 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.0111170800072813"
[1] "Log-like diff: 0.00141139826298442"
[1] "Log-like diff: 0.000161397428641408"
[1] "Log-like diff: 2.2397745002678e-05"
[1] "Log-like diff: 2.78974421874523e-06"
Running g = 5 ...
[1] "Initialization: 1"
[1] "Log-like diff: 1.06375966701933e-06"
Running g = 6 ...
[1] "Initialization: 1"
[1] "Log-like diff: 61.6606733135965"
[1] "Log-like diff: 220.279988254277"
[1] "Log-like diff: 343.597473475302"
[1] "Log-like diff: 118.657798326178"
[1] "Log-like diff: 89.3377189725218"
Running g = 7 ...
[1] "Initialization: 1"
[1] "Log-like diff: 4569.7505378941"
[1] "Log-like diff: 3260.30658412625"
[1] "Log-like diff: 427.290036720683"
[1] "Log-like diff: 812.707607252818"
[1] "Log-like diff: 292.797388408702"
Running g = 8 ...
[1] "Initialization: 1"
[1] "Log-like diff: 46.0926226393776"
[1] "Log-like diff: 35.3812812980884"
[1] "Log-like diff: 12.9785765602482"
[1] "Log-like diff: 0.756511837942664"
[1] "Log-like diff: 0.37566109213202"
Running g = 9 ...
[1] "Initialization: 1"
[1] "Log-like diff: 47.7096958256379"
[1] "Log-like diff: 66.4585845237957"
[1] "Log-like diff: 22.8816925488037"
[1] "Log-like diff: 314.666696217909"
[1] "Log-like diff: 181.581898960294"
Running g = 10 ...
[1] "Initialization: 1"
[1] "Log-like diff: 0.461052803007771"
[1] "Log-like diff: 0.256458121682254"
[1] "Log-like diff: 0.106759220704497"
[1] "Log-like diff: 0.0339550212833508"
[1] "Log-like diff: 0.011994087019815"
Running g = 11 ...
[1] "Initialization: 1"
[1] "Log-like diff: 28.7888473062168"
[1] "Log-like diff: 1.63534313289344"
[1] "Log-like diff: 0.63011593410125"
[1] "Log-like diff: 0.253746859510482"
[1] "Log-like diff: 0.0982710563896738"
Running g = 12 ...
[1] "Initialization: 1"
[1] "Log-like diff: 80.0953725504379"
[1] "Log-like diff: 5.29929211413723"
[1] "Log-like diff: 42.6136182485174"
[1] "Log-like diff: 14.7319905007355"
[1] "Log-like diff: 25.0172757256348"
$ICL


$profiles


$boxplots


$probapost_barplots


*************************************************
Model: Poisson
Transformation: none
*************************************************
Clusters fit: 2,3,4,5,6,7,8,9,10,11,12
Clusters with errors: ---
Selected number of clusters via ICL: 12
ICL of selected model: -3391000
*************************************************
Number of clusters = 12
ICL = -3391000
*************************************************
Cluster sizes:
 Cluster 1  Cluster 2  Cluster 3  Cluster 4  Cluster 5  Cluster 6  Cluster 7 
       276         73        112        172         41        302         24 
 Cluster 8  Cluster 9 Cluster 10 Cluster 11 Cluster 12 
       751        121        104        609        256 

Number of observations with MAP > 0.90 (% of total):
2772 (97.57%)

Number of observations with MAP > 0.90 per cluster (% of total per cluster):
 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7
 265       73        106       167       40        294       22       
 (96.01%)  (100%)    (94.64%)  (97.09%)  (97.56%)  (97.35%)  (91.67%) 
 Cluster 8 Cluster 9 Cluster 10 Cluster 11 Cluster 12
 743       116       100        599        247       
 (98.93%)  (95.87%)  (96.15%)   (98.36%)   (96.48%)  
Class: pca dudi
Call: dudi.pca(df = log(data + 0.1), center = TRUE, scale = TRUE, scannf = FALSE, 
    nf = 4)

Total inertia: 24

Eigenvalues:
     Ax1      Ax2      Ax3      Ax4      Ax5 
22.01676  1.12356  0.26987  0.11457  0.06979 

Projected inertia (%):
    Ax1     Ax2     Ax3     Ax4     Ax5 
91.7365  4.6815  1.1245  0.4774  0.2908 

Cumulative projected inertia (%):
    Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
  91.74   96.42   97.54   98.02   98.31 

(Only 5 dimensions (out of 24) are shown)

NULL

Meeting summary Antoine Sophie

Enquête sur la similarité entre cNF et CNF

  • Corrélations entre les réplicats à l’intérieur d’une condition et de l’autre sont faiblement supérieures à celles entre cNF et CNF (images investigations factorCO2)
Réseau CO2

Réseau CO2

  • Quand on compare CNF à une condition x et cNF à cette même condition x (6 conditions possibles pour x), on retrouve entre 40 et 70% de gènes en commun, suggérant toute fois des différences entre ces deux transcritômes (on aurait presque 100% de similarité sinon)

Application des ces méthodes à la tomate

  • La tomate semble répondre différemment dans certaines mesures : plus d’effet du CO2, effet moindre du fer, effet nitrate plutôt similaire.

  • Ontologies moins fournies pour la tomate

Réseau CO2

Réseau CO2

Relevance network sur les gènes qui répondent globalement à un facteur

  • DEG en commun entre les 4 comparaisons possibles pour l’effet d’un facteur (Venn diagrams)

  • Fait sur l’ensemble des transcriptômes (plus large que les transcriptômes sur lesquels les DEG ont été détectés)

  • Relevance Network fait comme Rodrigo. et Al, seuil sur la valeur de corréation et sur la pvalue, gènes triés sur leur centralité et connectivité

  • Visualisés dans igraph après Clustering

  • Visualisés dans Cytoscape, pus clustering de communautés (pluggins) et analyse d’enrichissement d’ontologies

Réseau CO2

Réseau CO2

Début de biblio sur les méthodes d’inférence de GRN

  • cédur
 

A work by Océane Cassan

oceane.cassan@supagro.fr